Validation of a plasma-based comprehensive cancer ......2018/04/24 · Author Manuscript Published...

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Validation of a plasma-based comprehensive cancer genotyping assay utilizing orthogonal

tissue- and plasma-based methodologies

Justin I. Odegaard MD PhD1*, John J. Vincent PhD1, Stefanie A. Mortimer PhD1, James Vowles

PhD1, Bryan C. Ulrich2, Kimberly C. Banks MGC1, Stephen R. Fairclough PhD1, Oliver A. Zill

PhD1,3, Marcin Sikora PhD1, Reza Mokhtari MS1, Diana Abdueva PhD1, Rebecca J. Nagy

MGC1, Christine E. Lee MS1, Lesli A. Kiedrowski MS MPH1, Cloud P. Paweletz PhD2, Helmy

Eltoukhy PhD1, Richard B. Lanman MD1, Darya I. Chudova PhD1, AmirAli Talasaz PhD1

1Guardant Health, Redwood City, CA 2Dana-Farber Cancer Institute, Boston, MA 3Genentech, South San Francisco, CA

*Corresponding Author: Justin I. Odegaard, [email protected], 505 Penobscot

Dr, Redwood City, CA

Running Title: Validation of a comprehensive cancer liquid biopsy test

Conflict of interest statement

JIO, JJV, SAM, JV, KCB, SRF, MS, RM, DA, RJN, CEL, LAK, HE, RBL, DIC, and AT are

employees and shareholders in Guardant Health. BCU, OAZ, and CPP have no potential

conflicts of interest to declare.

Research. on September 7, 2021. © 2018 American Association for Cancerclincancerres.aacrjournals.org Downloaded from

Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on April 24, 2018; DOI: 10.1158/1078-0432.CCR-17-3831

mailto:[email protected]

http://clincancerres.aacrjournals.org/

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TRANSLATIONAL RELEVANCE

Plasma-based comprehensive tumor genotyping expands access to standard-of-care precision

oncology to patients previously ineligible due to barriers associated with tissue sampling.

Despite this and the current clinical use of multiple such “liquid biopsies”, comprehensive,

structured validation studies of relevant patient populations of meaningful size using validated

orthogonal comparator methods are lacking, which leaves clinicians without the knowledge

necessary to properly vet and interpret available options. Here, we describe definitive analytical

and clinical validation studies of a comprehensive tumor-derived cell-free DNA sequencing

assay for clinical genotyping of advanced solid tumors and report clinical experience applying

this technology in the clinical care of 10,593 consecutive patients. These data establish this

technology as a clinically effective, accurate tumor genotyping alternative for patients for whom

invasive tissue acquisition procedures are infeasible.




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ABSTRACT

Importance: Liquid biopsies are powerful tools that enable non-invasive genotyping of advanced

solid tumors; however, comprehensive, structured validation studies employing validated

orthogonal comparator methods are lacking.

Objective: To analytically and clinically validate a circulating cell-free tumor DNA sequencing

test for comprehensive tumor genotyping and demonstrate its clinical feasibility.

Design: Analytical validation was conducted according to established principles and guidelines.

Blood-to-blood clinical validation comprised blinded external comparison to clinical digital

droplet PCR across 222 consecutive biomarker-positive clinical samples. Blood-to-tissue clinical

validation comprised comparison of Digital Sequencing calls to those documented in the

medical record of 543 consecutive lung cancer patients. Clinical experience was reported from

10,593 consecutive clinical samples.

Results: Digital Sequencing technology enabled variant detection down to 0.02%-0.04% allelic

fraction/2.12 copies with ≤0.3%/2.24-2.76 copies 95% limits of detection while maintaining high

specificity (prevalence-adjusted PPVs >98%). Clinical validation using orthogonal plasma- and

tissue-based clinical genotyping across >750 patients demonstrated high accuracy and

specificity (PPAs and NPAs >99% and PPVs 92-100%). Clinical use in 10,593 advanced adult

solid tumor patients demonstrated high feasibility (>99.6% technical success rate) and clinical

sensitivity (85.9%), with high potential actionability (16.7% with FDA-approved on-label

treatment options; 72.0% with treatment or trial recommendations), particularly in non-small cell

lung cancer where 34.5% of patient samples comprised a directly targetable standard-of-care

biomarker.

Conclusions: High concordance with orthogonal clinical plasma- and tissue-based genotyping

methods supports the clinical accuracy of Digital Sequencing across all four types of targetable

genomic alterations. Digital Sequencing’s clinical applicability is further supported by high rates

of technical success and biomarker target discovery.




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INTRODUCTION

Modern systemic cancer therapy is evolving away from cytotoxic polychemotherapy to embrace

novel therapeutics targeting molecular characteristics specific to individual patients’ tumors that

deliver superior clinical outcomes at reduced toxicity and overall cost(1,2). Indeed, precision

oncology is the preferred standard of care in National Comprehensive Cancer Network (NCCN)

treatment guidelines(3) for advanced non-small cell lung cancer (NSCLC), colorectal cancer,

melanoma, breast cancer, gastric and gastroesophageal adenocarcinoma, and gastrointestinal

stromal tumors, and is rapidly maturing in multiple other indications. Due to the increasing

diversity of targeted therapies and associated molecular biomarkers, serial testing via traditional

singleplex methods, such as PCR and FISH, has become impractical in some diseases(4).

Congruently, the 2017 NCCN NSCLC practice guidelines explicitly recommend testing via

“broad molecular profiling” by next-generation sequencing (NGS) methods validated to detect

multiple variants simultaneously across all four genomic variant classes (base substitutions

(SNVs), insertions/deletions (indels), gene amplifications (CNAs), and gene rearrangements

(fusions))(4).

Even when broad molecular profiling is adopted, however, precision oncology has historically

remained dependent on tissue-based biomarker assessment, which limits patient benefit in four

primary ways. First, tissue sampling—whether by core needle biopsy, surgical biopsy, or fine-

needle approaches—is necessarily invasive and burdened with the morbidity, mortality, costs,

and delays associated with these procedures(5–7). Second, tissue sampling has variable but

substantial failure rates due to patient ineligibility, procedural/sampling failure, or material

insufficiency, which range from 20% to more than 50% in second-line advanced lung cancer

patients(8–10). Third, tissue sampling is unavailable for a substantial minority of patients due to

medical contraindication, patient unwillingness, and/or logistical concerns; congruently, extant

data on genotyping rates demonstrate only moderate adherence to practice guidelines in the 1st

line (65-75%) and poor adherence in the 2nd and later (<25%)(11,12). Fourth, tissue sampling is

necessarily limited to a single, small tumor focus, which may not accurately represent all

clinically relevant biomarkers due to spatial and temporal heterogeneity(13–16). Taken together,

these data describe critical and avoidable missed treatment opportunities for patients with

advanced solid tumors(8,14,17).

The advent of tumor genotyping from peripheral blood (“liquid biopsy”) via circulating cell-free

tumor DNA (ctDNA) obviates these limitations and expands access to standard-of-care

precision oncology to patients previously ineligible due to barriers associated with tissue

sampling(4,8,9,18–22). Despite this, comprehensive, structured validation studies of relevant

patient populations of meaningful size using validated orthogonal comparator methods are

lacking, with existing studies failing to adhere to established comparison study design

principles(23). Here, we describe the analytical (technical characterization using controlled and

often contrived samples) and clinical (characterization using real-world patient samples,

contexts, and comparators) validation of a comprehensive ctDNA sequencing assay for clinical

genotyping of advanced solid tumors including large-scale comparisons to orthogonal clinical

plasma- and tissue-genotyping methods. We further report our experience applying this

technology in the clinical care of 10,593 consecutive patients in our Clinical Laboratory




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Improvement Amendment (CLIA)-certified, College of American Pathologists (CAP)-accredited,

New York State Department of Health (NYSDOH)-approved laboratory, which represents the

largest published dataset of clinical liquid biopsy experience to date.

MATERIALS AND METHODS

Blood draw, shipment, and plasma isolation

All patient samples were collected and processed in accordance with the Guardant360 clinical

blood collection kit instructions (Guardant Health, Inc.). Briefly, 2 x 10ml of peripheral venous

blood was collected in Streck Cell-Free DNA BCT (Streck, Inc.), packaged into a provided

padded thermal insulation block within a secondary biohazard containment barrier, and shipped

overnight to Guardant Health at ambient temperature. While samples were accepted up to 7

days after blood collection, the median draw-to-receipt interval was 1.43 days. Upon receipt,

samples were accessioned and proceeded immediately to plasma isolation (median

accessioning-to-isolation interval 17 minutes). To isolate plasma, whole blood was centrifuged

(1,600 x g for 10min at 10°C), and the resulting supernatant was clarified by additional

centrifugation (3,220 x g for 10min at 10°C). Clarified plasma was transferred to a fresh tube

and stored at 2°C for immediate extraction or stored at -80°C.

cfDNA extraction, library preparation, and sequencing

All cfDNA extraction, processing, and sequencing was performed in a CLIA-certified, CAP-

accredited laboratory (Guardant Health, Inc.) as previously described(24). Briefly, cfDNA was

extracted from the entire plasma aliquot prepared from a single 10ml tube described above

(QIAmp Circulating Nucleic Acid Kit, Qiagen, Inc.), which yielded a median of 48.5ng of cfDNA

(range 2-1,050ng). 5-30ng of extracted cfDNA (66% of samples processed with 30ng input) was

labeled with non-random oligonucleotide barcodes (IDT, Inc.) and used to prepare sequencing

libraries, which were then enriched by hybrid capture (Agilent Technologies, Inc.), pooled, and

sequenced by paired-end synthesis (NextSeq 500 and/or HiSeq 2500, Illumina, Inc.). Contrived

analytical samples were generated using similarly prepared cfDNA from healthy donors

(AllCells, Inc.) and cfDNA isolated as above from the culture supernatant of model cell lines

(ATCC, Inc.; Sigma, Inc.) and serially size-selected using Agencourt Ampure XP beads

(Beckman Coulter, Inc.) until no detectable gDNA remained. Separate sequencing controls are

utilized for SNVs and CNAs/fusions/indels (CFI). The SNV control comprises a mixture of

healthy donor cfDNA pooled to target germline SNVs to 0.5%, 2.5%, and 6% allelic fraction. The

CFI control comprises cell lines with known CNAs, fusions, and indels diluted into healthy donor

cfDNA.

Bioinformatics analysis and variant detection

All variant detection analyses were performed using the locked clinical Guardant360

bioinformatics pipeline and reported unaltered by post-hoc analyses. All decision thresholds

were determined using independent training cohorts, locked, and applied prospectively to all

validation and clinical samples. As previously described(24), base call files generated by

Illumina’s RTA software (v2.12) were de-multiplexed using bcl2fastq (v2.19) and processed with

a custom pipeline for molecule barcode detection, sequencing adapter trimming, and base




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quality trimming (discarding bases below Q20 at the ends of the reads). Processed reads were

then aligned to hg19 using BWA-MEM (arXiv:1303.3997v2) and used to build double-stranded

consensus representations of original unique cfDNA molecules using both inferred molecular

barcodes and read start/stop positions. SNVs were detected by comparing read and consensus

molecule characteristics to sequencing platform- and position-specific reference error noise

profiles determined independently for each position in the panel by sequencing a training set of

62 healthy donors on both the NextSeq 500 and HiSeq 2500. Observed positional SNV error

profiles were used to define calling cut-offs for SNV detection with respect to the number and

characteristics of variant molecules, which differed by position but were most commonly ≥2

unique molecules, which in an average sample (~5,000 unique molecule coverage) corresponds

to a detection limit of ~0.04% VAF. Indel detection used two methods. For short (<50-70bp)

indels, a generative background noise model was constructed to account for PCR artifacts

arising frequently in homopolymeric or repetitive contexts, allowing for strand-specific and late

PCR errors. Detection was then determined by the likelihood ratio score for observed feature-

weighted variant molecule support versus background noise distribution. Detection of indels

>>50bp relies on secondary analysis of soft-clipped reads using methods described in the

fusion section below and is only performed to detect specific genomic events (e.g. MET exon

14-skipping deletions). Reporting thresholds were event-specific as determined by performance

in training samples but were most commonly at least one unique molecule for clinically

actionable indels, which in an average sample corresponds to a detection limit of ~0.02% VAF.

Fusion events were detected by merging overlapping paired-end reads to form a representation

of the sequenced cfDNA molecule, which was then, aligned, mapped to initial unique cfDNA

molecules based on molecular barcoding and alignment information, including soft clipping.

Soft-clipped reads were analyzed using directionality and breakpoint proximity to identify

clusters of molecules representing candidate fusion events, which were then used to construct

fused references against which reads soft-clipped by the aligner on the first pass were

realigned. Specific reporting thresholds were determined by retrospective and training set

analyses but were generally ≥2 unique post-realignment molecules meeting quality

requirements, which in an average sample corresponds to a detection limit of ~0.04% VAF. To

detect CNAs, probe-level unique molecule coverage was normalized for overall unique molecule

throughput, probe efficiency, GC content, and signal saturation and robustly summarized at the

gene level. CNA determinations were based on training set-established decision thresholds for

both absolute copy number deviation from per-sample diploid baseline and deviation from the

baseline variation of probe-level normalized signal in the context of background variation within

each sample’s own diploid baseline. Per-sample relative tumor burden was determined by

normalization to the mutational burden expected for tumor type and ctDNA fraction and reported

as a z-score.

Analytical validation approach

In the analytical validation of the assay, we adhered to established CLIA, Nex-StoCT Working

Group, and Association of Molecular Pathologists/CAP guidance regarding performance

characteristics and validation principles. To define clinically meaningful performance, the entire

assay workflow was mapped against possible clinical sample contexts, points and mechanisms

of possible failure or inaccuracy identified, and validation studies designed to systematically




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examine each of these critical areas as per Clinical and Laboratory Standards (CLSI) guidance

EP23-A. As such, the validation cohort comprises broad variant diversity and is heavily enriched

for alterations with low VAFs and/or near decision boundaries. All studies were conducted using

contrived materials and subsequently verified using patient samples, both of which were

themselves validated using orthogonal methods and reference materials wherever possible.

Additionally, all studies were conducted at standard input amounts using the locked clinical

Guardant360 bioinformatics pipeline and results reported unaltered by post-hoc analyses. All

decision thresholds were predetermined using independent training cohorts, locked, and applied

prospectively to all validation and clinical samples.

To assess sensitivity for sequence variants (SNVs, indels, and fusions), cfDNA samples with

orthogonally-confirmed (e.g. ddPCR, tissue genotyping, exome sequencing, published

characterization studies) variants were titrated into donor cfDNA targeting ≥5 pre-specified

VAFs at both standard (30ng) and minimum (5ng) cfDNA inputs. Multiple replicates of each

titration point were then processed and used to calculate the detection probability for both

clinically actionable and background variants and define the 95% limit of detection (LOD) both

empirically and by probit regression. CNAs, in contrast, are not detectable at the level of

individual molecules and are instead defined by statistical overrepresentation of reference

sequences in the cfDNA. As such, we utilized the CLSI “Classical Approach” (CLSI EP17-A2) to

statistically determine copy number LOD based on variation observed in titration series

replicates and normal samples by calculating the limit where estimated copy number and z-

score values jointly have a 95% probability of exceeding the decision threshold.

Accuracy studies comprised orthogonally verified contrived and clinical samples representing

the entire reportable range; however, cohorts were heavily enriched for variants at near and

below the LOD and in unusual sequence contexts. Prevalence-adjusted PPV as a function of

allelic frequency was defined as follows: 𝑃𝑃𝑉𝑖 = (𝐹𝑖

𝑐−𝐹𝑖𝑁)

𝐹𝑖𝑐 , where 𝐹𝑖

𝑐 is per-sample frequency of

somatic calls in clinical samples within VAF range 𝑖, and 𝐹𝑖𝑁 is per-sample frequency of false

positive calls in known negative samples in allelic range 𝑖. 2,585 previously analyzed

consecutive clinical samples were used to define somatic alteration frequency by VAF.

Frequency of false positive calls was defined using a set of 408 validation runs (324 LOD pools

and 84 healthy cfDNA donor runs across both NextSeq and HiSeq instruments) for which truth

was orthogonally defined. Analytical specificity was determined by analysis of pooled samples

at high dilution that had been orthogonally defined at high VAF. Precision was determined

utilizing in-process positive controls from the clinical sequencing workflow and pooled clinical

samples.

Orthogonal validation methods

For in-house assays ddPCR, sequence variant analyses were performed using the Bio-Rad

droplet digital PCR platform with QX200 droplet reader (Bio-Rad, Inc.). SNVs and indels were

quantified using variant-specific PCR primer pairs and wild-type or mutant-specific fluorescent

hydrolysis probes (Bio-Rad, Inc., IDT, Inc.). Copy number determination was assessed using a

compendium of multiplexed PCR assays against reference genes in combination with target-




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specific PCR assays. Copy number was determined from the average of the reference gene

signal relative to the total target signal. For external clinical ddPCR studies, de-identified clinical

samples were submitted to the Dana-Farber Cancer Institute Translational Research Laboratory

for ddPCR testing as previously described(25). The variant test was determined from available

assays (EGFR L858R, exon 19 deletions, and T790M; KRAS G12X/G13D; BRAF V600X; ESR1

D538G and Y537C; and PIK3CA H1047R) by the Laboratory’s choice based on a provided

clinical diagnosis; no variant or result information was provided. For external tissue

concordance, tissue genotyping was conducted as part of routine clinical care and comprised a

variety of methods based on PCR, sequencing, ISH, or immunohistochemistry.

All studies were conducted in accordance with recognized ethical guidelines (e.g., Declaration

of Helsinki, CIOMS, Belmont Report, U.S. Common Rule) and with a waiver of patient consent

by an institutional review board-approved protocol.

RESULTS

Assay design

The assay, Guardant360, utilizes NGS-based Digital Sequencing (DS)(24) to comprehensively

profile 73 cancer-related genes (Supplemental Figure 1) comprising both therapeutically-

relevant biomarkers—including those for all approved and many investigational drugs and

clinical trials—and cancer-specific biomarkers that may be used to establish both ctDNA

presence and quantity. Briefly, cell-free DNA (cfDNA) is extracted from stabilized whole blood,

labeled at high efficiency with non-random oligonucleotide adapters (‘molecular barcodes’), and

used to prepare sequencing libraries, which are then enriched using hybrid capture and

sequenced (Figure 1). Sequencing reads are then used to reconstruct each individual cfDNA

molecule present in the original patient sample with high-fidelity using proprietary double-

stranded consensus sequence representation.

The combination of high-efficiency (80-90%) input molecule labeling, optimized assay

conditions, and hybridization probe design allows DS to recover 60-85% of all input molecules

post-alignment (Figure 2a), depending on cfDNA input, across the entire range of G:C content

(Figure 2b) with highly uniform coverage (13% quartile-based coefficient of variation with >96%

of exonic panel positions covered above 0.2x median panel coverage, Figure 2c). Additionally,

DS’s in silico molecule reconstruction suppresses substitution and indel errors intrinsic to NGS

workflows by more than three orders of magnitude relative to standard sequencing approaches,

from ~10-3.5 to ~10-6.5 per base (Figure 2d,e), which allows accurate variant calling down to

variant allelic fractions (VAFs) of 0.01%/1 molecule for indels and 0.04%/2 molecules for SNVs

and fusions (summarized in Supplemental Table 1). Critically, >50% of molecules are

reconstructed from both strands of the original cfDNA molecule, greatly increasing consensus

sequence fidelity and specificity over other previously published approaches(26–32), which

integrate both strands in only 12% or fewer of recovered unique molecules.

SNV detection performance

To validate the sensitivity and accuracy of SNV detection, donor cfDNA comprising 43 non-




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redundant germline variants defined by orthogonal exome analysis of leukocyte genomic DNA

(gDNA) and patient sample cfDNA comprising 12 actionable somatic SNVs confirmed by digital

droplet PCR (ddPCR) was titrated to 5 pre-specified VAFs in triplicate bracketing the estimated

95% limit of detection (LOD95) in 753 total observations. An additional patient cfDNA pool

comprising 67 diluted germline variants and 22 actionable somatic SNVs was also processed at

VAFs representative of clinical somatic variant prevalence. Within the titration series, the LOD95

was determined empirically to be 0.3% (0.20% by probit) for actionable and slightly above 0.4%

(94% detection rate, 0.34% by probit) for variants of uncertain significance (Figure 3a,

Supplemental Figure 2a). Overall, detection above the LOD95 was high (122/122, 100%), with

high prevalence-adjusted PPV (99.2%). Importantly, even at VAFs below the LOD95 (0.05%-

0.25%), SNV detection remained moderate (35/55, 63.8%) and accuracy high (PPV 96.3%,

Figure 3c), which is critical for ctDNA analyses where many clinically relevant alterations are

present at very low levels. When variants associated with clonal hematopoiesis (identified by

reproducible detection on replicate analysis within and between sequencing platforms) were

excluded, PPV across the validation dataset rose to 99.96%. Similarly, in 667 serial replicates of

a well-defined cfDNA reference material used as an in-process sequencing control, only 20 of

42,496 total variants (VAF 0.5%-6%) detected were unexpected, corresponding to an analytical

PPV of 99.95% and a per-sample specificity of 97%. Importantly, in all studies, PPV of clinically

actionable SNVs was 100% both above and below the LOD95. SNV detection also maintained

quantitative accuracy at VAFs near the LOD95; across 871 observations between 0.05% and

1.0%, the correlation between observed and expected VAFs was high (r2=0.84, y=1.06,

Supplemental Figure 3a).

Indel detection performance

To validate indel detection performance, samples comprising 3 ddPCR-confirmed driver and 13

tumor suppressor indels across 11 genes were diluted to create an 8-point titration series

comprising 200 near-LOD observations. The LOD95 was established empirically at 0.2% and

0.7% for driver and tumor suppressor indels, respectively (0.11% and 0.43% by probit, Figure

3a, Supplemental Figure 2b). These data were supplemented with samples comprising 25

additional indels across 10 genes verified by either literature (cell lines), tissue genotyping

(patient samples), or ddPCR (both) to determine an indel identification accuracy of 100%

(34/34). Prevalence-adjusted PPV for all indel calls was estimated at 98.2% both above and

below the LOD95. PPV for actionable indels remained 100% across the entire reportable range

of VAFs. Per-sample analytical specificity using donor cfDNA was 100% (42/42) and 98%

(49/50) for undiluted and pooled donors and 99.7% (665/667) in sequencing control material

described above.

Fusion detection performance

To assess fusion detection performance, samples comprising 4 cell line- and 8 patient-derived

cfDNA fusions were titrated to generate 93 near-LOD observations. The LOD95 was determined

empirically to be 0.2% (0.22% by probit, Figure 3a, Supplemental Figure 2c). Including

additional cell lines and patient samples, analytical accuracy was determined to be 95% (20/21

unique fusions), and analytical PPV was 100% both above and below the LOD95. Per-sample

analytical specificity using donor cfDNA was 100% for undiluted (42/42) and pooled (50/50)




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donors and in sequencing control material (667/667).

CNA detection performance

To validate CNA detection performance, cell line cfDNA harboring known amplifications of

ERBB2, MET, EGFR, MYC, CCND1, and CCNE1 was used to bracket the expected LOD95 in

120 near-LOD observations. LOD95 estimates constructed for each gene independently ranged

from 2.24 copies (ERBB2) to 2.76 (CCND2, the smallest gene on the panel), median 2.44

(Figure 3a, Supplemental Figure 2d). Using multiplex-baselined ddPCR, we validated CNA

accuracy in 14 cell lines comprising 43 amplifications (3-74 copies, median 4) in 10 genes.

Importantly, DS results correlated closely with copy number results derived from both ddPCR

(r2=0.98, slope=1.03) and Cancer Cell Line Encyclopedia (CCLE) microarray measurements

(r2=0.84, slope=1.16, Supplemental Figure 3b,c). As no unexpected positives were observed in

the 257 characterized samples within the validation dataset, CNA detection PPV was

determined to be 100%. Using well-characterized cfDNA CNA-specific sequencing control

material, analytical PPV was measured to be 98.3% (226/230). Analytical specificity using

healthy donor cfDNA was 100% for undiluted (42/42) and pooled (50/50) donors and 99.9%

(3,712/3,716) in CNA-specific sequencing control material.

Variant detection precision

To determine run-to-run precision, we analyzed positive sequencing controls included in each

batch of clinical samples. Across 375 consecutive runs of one SNV control lot, precision of

exonic SNVs was 99.9% for 6% VAF (n=749/750), 99.5% for 2.5% VAF (n=8,583/8,625), and

97.6% for 0.5% VAF (n=6,954/7,125). Across those same 375 runs, which comprised multiple

copy number-fusion-indel control lots, indel (unique n=4, 1.0%-7.5% VAF), fusion (unique n=6,

1.0%-2.0% VAF), and copy number precision (unique n=6, 2.4-5.2 copies) was 100%.

Quantitative VAF and copy number measurements were consistent over more than 370 runs

(Figure 3b, Supplemental Figure 4) with 95% of SNVs, indels, fusions, and CNAs within 32.5%,

26.1%, 73.6%, and 5.2% of targeted levels, respectively.

Clinical validation studies

To validate clinical accuracy, we submitted 222 consecutive EGFR, KRAS, BRAF, and/or

ESR1-positive NSCLC, colorectal cancer, or breast cancer specimens received for clinical

testing to the Dana-Farber Cancer Institute for blinded analysis using a highly validated clinical

ddPCR panel(25) (Figure 4a,b, Supplemental Table 2). DS and ddPCR were concordant for 269

positive calls (0.1%-94% VAF, PPA 99.6%) and 308 negative (NPA 97.8%). Only a single

variant, KRAS G12C, was detected by ddPCR but not DS (at 0.18%, below DS’s LOD95), while

DS detected 3 EGFR exon 19 deletions and 4 T790M SNVs (0.10%-0.39% VAF) at or below

ddPCR’s reportable range(25), most probably due to DS’s greater per reaction cfDNA input (DS

interrogates up to 30ng in a single reaction, whereas ddPCR spreads this across 3 separate

reactions). Importantly, all discordant results occurred below either one or both assays’ LOD

and/or reportable range. Moreover, clinical follow up of such samples demonstrated support for

positive calls in 6 of 8 discordant cases (Supplemental Table 3).

We similarly assessed the accuracy of copy number determination relative to ddPCR in 70




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samples with detectable CNAs in 8 genes (copy numbers 2.33-48.0) and 49 samples without.

Together, ddPCR and DS calls were concordant for 63/70 positive calls and all 49 negative

(PPA 90%, NPA 100%, Figure 4a). Importantly, all 7 discordant calls were near the decision

threshold of one or both assay platforms; outside of this region, concordance was 100%.

To validate quantitative accuracy, we compared DS’s VAFs and copy numbers to ddPCR

measurements (Figure 4b,c). As expected, SNV and indel VAFs and CNA copy numbers were

highly correlated (r2=0.994, 0.995, and 0.943 respectively; y=0.944, 0.922, and 0.980,

respectively).

To assess ctDNA-tissue genotyping concordance, we conducted a blinded, IRB-approved

retrospective review of samples received for clinical testing, distinct from the studies above, in

which we extracted external molecular testing results from submitted pathology reports and

determined whether variants identified by DS were present by established clinical genotyping

methods. From an initial cohort of 6,948 consecutive NSCLC patients, we identified 543 for

whom DS calls could be compared to external tissue genotyping results. Across seven genomic

biomarkers (EGFR, ALK, ROS1, RET, BRAF, MET, and KRAS) clinically relevant for primary

therapy selection, the positive concordance was 92-100% (Figure 5). Importantly, post-hoc

clinical follow-up of the 3 patients positive for ALK fusions by DS but negative by fluorescent in

situ hybridization revealed that these patients had not only been treated with crizotinib

irrespective of the diagnostic discordance but had also responded clinically as assessed by

clinician judgment (33).

Clinical experience

Validation studies, such as those described above, are critical to establish technical

performance metrics and verify clinical performance; however, clinical utility is predicated on the

ability to apply a test in real-world clinical care and return results in a reliable and timely manner.

Moreover, test performance on large numbers of patients can often provide insights into test

accuracy than smaller, controlled validation studies. To these ends, we analyzed consecutive

clinical samples submitted to our CLIA-certified, CAP-accredited clinical laboratory. As our test

is intended only for systemic therapy selection, all samples are derived from patients with

advanced tumors. Within the study period, 10,593 samples were submitted with 10,585 (99.9%)

comprising sufficient cfDNA for analysis and 10,547 successfully reported (99.6% analytical

success rate, Figure 6a). ctDNA detection rate overall was high (85.9%), predominantly driven

by NSCLC (87.7%), colorectal (85.0%), and breast (86.8%); however, certain tumors, e.g.

primary central nervous system malignancies, were less likely to comprise detectable ctDNA,

due to smaller typical tumor volumes and anatomic considerations such as the blood-brain

barrier (Figure 6b).

Alteration prevalence was markedly enriched at low VAFs (median VAF 0.46%) even when

restricted to targetable driver mutations (Figure 6c), emphasizing need for highly sensitive

diagnostics. At least one variant with therapeutic or clinical trial connotations was identified in

72.0% (7,591/10,547) of samples, and while individual patients typically comprised fewer than

two per sample, the superset comprised 3,479 unique alterations, underscoring the importance




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of broad genomic profiling. Congruent with previous reports of high ctDNA-gDNA

concordance(8,9,24,34,35), per-variant and VAF-normalized relative tumor mutation burden

landscape analyses closely match those of previous reports (Figure 6d, Supplemental Figure 5)

(36–38). At least one of 155 total unique variants associated with FDA-approved on-label

therapies was identified in 16.7% (1,766/10,547) of samples, and at least one of 133 total

unique variants associated with resistance to these approved drugs was identified in 13.9%

(1,467/10,547). In colorectal cancer patients, 66.7% (548/819) comprised alterations associated

with resistance to anti-EGFR antibody therapy, only 54.9% (301/548) of which were detectable

by common (codons 12/13) KRAS testing (Figure 6e). In 4,521 NSCLC patients, 34.5%

comprised at least one alteration directly targetable with standard-of-care therapeutics (Figure

6f), compatible with previously reported alteration frequencies(36,37).

DISCUSSION

Precision oncology is associated with improved outcomes, reduced adverse effect profiles, and

reduced overall cost; however, its application is limited by reliance on tissue-based biomarker

assessment, which shackles it to invasive biopsy procedures associated with substantial cost,

morbidity, mortality, and failure rates. In contrast, ctDNA-based liquid biopsies decouple

biomarker assessment from tissue, enabling tumor-specific genotyping using safe, inexpensive,

and near-painless peripheral blood sampling. While promising, successful implementation of

liquid biopsy is fraught with challenges including ctDNA’s short fragment lengths (~165bp),

scant material (median 48ng per 10ml whole blood vs. micrograms in typical tissue sections),

low tumor representation (VAFs typically 10s- to 100s-fold lower than tissue), and absent

reference materials and methods.

We have developed an NGS-based ctDNA diagnostic that overcomes these challenges to

provide highly accurate tumor-specific genotyping for all guideline-recommended advanced

solid tumor somatic biomarkers from a single peripheral blood draw. This test demonstrates

exceptional sensitivity (LOD95 ≤0.3% for SNVs, indels, and fusions and near 2.2 copies for

CNAs), while maintaining high PPV (>98%) even in sample cohorts enriched for alterations at or

below the applicable LOD. Specificity and precision are similarly high, allowing accurate variant

identification as low as 0.02%-0.04%, which is critical for clinical ctDNA analysis as many

relevant alterations are present at very low levels. Performance was verified in 349 clinical

samples using internal and blinded external comparison to clinical ddPCR, which demonstrated

very high qualitative and quantitative concordance. Analysis of 543 matched tissue genotyping

results demonstrated high PPVs relative to standard-of-care tissue diagnostics. Of note, clinical

response to targeted therapy in the three ALK fusion ctDNA-positive/tissue-negative patients is

congruent with previous reports(8,39,40), highlighting a weakness of method comparison to

imperfect reference methodologies as compared to the gold standard of clinical response.

Clinical sensitivity was not assessable in this tissue genotyping comparison study; however,

other studies have reported PPAs relative to tissue varying substantially between 52% and

100%(8,9,21–24,34,41–44), with most between 70% and 90%, depending on the specific

biomarker, tissue test, clinical indication, overlap of comparator panels and capabilities, and

time elapsed between tissue and plasma sampling(21,23).




13

When used clinically for 10,593 consecutive patients, DS demonstrated robust technical

success (>99.6%), ctDNA detection (85.9%), and rapid result delivery (median time from sample

receipt to report, 7 days). While at least one variant with therapeutic or clinical trial connotations

was identified in 72.0% of samples, these comprised 3,479 unique alterations in aggregate,

underscoring the importance of broad genomic profiling. Importantly, 16.7% of samples

harbored variants with on-label treatment recommendations, while 13.9% harbored variants

predictive of resistance to the same. Underscoring the importance of highly sensitive

diagnostics, >50% of alterations were detected below 0.5% VAF.

These studies comprise the largest published ddPCR and tissue concordance series and

demonstrate near-perfect concordance for driver alterations. Similarly, mutational prevalence

and mutational burden analyses mirror findings from previous tissue-based reports(36–38).

These high concordances strongly support existing evidence demonstrating the validity and

utility of DS-guided treatment, which includes 17 clinical outcome studies for genomically-

targeted therapies(8,9,19–22,34,42–48) and TMB-based(49) immunotherapy. In contrast to

limited/hotspot ctDNA panels, Guardant360’s 73-gene design also allows interpretation of

negative findings (required for effective biopsy prevention) and early detection of

immunotherapy resistance(50,51) through inclusion of all major driver oncogenes and tumor

suppressors.

These data demonstrate applicability of ctDNA-based genotyping to treatment paradigms based

on tissue genotyping; however, ctDNA has the potential to not only match but to surpass tissue

genotyping in four important ways: First, ctDNA, as a circulating analyte, is derived from the

entire tumor burden and thus has potential to capture tumor heterogeneity that tissue-based

methods, which sample only a small focus, cannot achieve. Indeed, plasma-based detection of

ALK fusions not present in focal tissue biopsies in the tissue comparison study above and those

patients’ subsequent therapeutic responses illustrates the potential impact of tumor

heterogeneity. Second, the non-invasive nature of liquid biopsy enables longitudinal analyses

not feasible with invasive tissue biopsies. Testing for acquired therapy resistance is a current

application of longitudinal analysis with proven therapeutic relevance; however, other potential

applications include therapy monitoring, early prediction of immunotherapy efficacy, and disease

surveillance. Third, as ctDNA production is proportional to tumor growth, the fastest-growing

tumor clones shed the most material, which makes ctDNA enriched for the most biologically

aggressive—and likely most clinically relevant—tumor cells. Fourth, the quantitative accuracy of

ctDNA-based genotyping enables discrimination of dominant versus subclonal alterations over

both time and space and may potentially allow more accurate therapeutic targeting(13–16).

In summary, we present the analytical and clinical validation of a highly accurate non-invasive

option for comprehensive tumor genomic profiling of advanced solid tumors, including high

concordance to both orthogonal clinical plasma- and tissue-based genotyping assays, and

present real-life clinical use data in >10,000 advanced cancer patients demonstrating its

feasibility and value in delivering both established and novel benefits of precision oncology to

patients for whom tissue is unavailable.




14

Author contribuction statement

JIO, KCB, CPP, DIC, and AT participated in study design. JIO, JJV, SAM, JV, BCU, KCB, SRF,

OAZ, MS, RM, DA, RJN, CEL, LAK, CPP, and DIC performed experiments and/or data

analyses. JIO, KCB, CPP, DIC, RBL, HE, and AT participated in manuscript writing.




15

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FIGURE LEGENDS

Figure 1. Digital Sequencing-based ctDNA assay workflow. (a) cfDNA is extracted from

stabilized peripheral whole blood, (b) labeled with oligonucleotide barcodes at high efficiency,

and (c) up to 30ng is used for library preparation. (d) Sequencing libraries are enriched using

hybrid capture and sequenced to an average depth of ~15,000x. (e) Individual unique input

molecules are then bioinformatically reconstructed using barcode and sequence data to

suppress analytical error modes. (f) Somatic variants are deconvoluted from germline and

reported by clinical priority with both treatment and clinical trial annotations.

Figure 2. Technical assay performance characteristics. (a) Unique molecule recovery post-

sequencing as a function of input. (b,c) Unique molecule coverage as a function of GC content

(b) and per position as a fraction of median coverage (c). Total intrinsic (d) and specific base

substitution (e) error rates after each layer of DS error correction for 42 healthy donor cfDNA

samples. SE, single-end reads; PE, paired-end reads; MOL, molecular barcoding; DS, Digital

Sequencing.

Figure 3. Somatic variant detection performance. (a) Probit plot of detection probability as a

function of allelic fraction for oncogenic driver and VUS SNVs, oncogene and tumor suppressor

indels, oncogenic fusions, and 6 different gene amplifications. Sequence variants correspond to

the lower x-axis (allelic fraction); gene amplifications correspond to the upper x-axis (copy

number). Empirical observations include matched color. Horizontal grey line indicates 95%

detection probability. (b) Normalized allelic fraction or copy number across 375 consecutive

SNV and copy number-fusion-indel control runs. Number of observations across all replicates

indicated. Horizontal grey line indicates allelic fraction truth as indicated by orthogonal testing.

(c) Positive predictive value (PPV) by allelic fraction for all SNVs and indels and those

associated with a therapy or clinical trial adjusted by variant prevalence in a training set of 5,285

consecutive clinical samples. VUS, variant of uncertain significance; TS, tumor suppressor.

Figure 4. Concordance between DS and digital PCR in clinical samples. (a) Positive

concordance between DS and clinical ddPCR for 222 SNVs (blue) and 55 indels (gray) by VAF

and 70 CNAs (green) by copy number plotted as probit model estimates (bold lines) with 95%

confidence intervals (thin lines). (b,c) Quantitative correlation with linear regression for SNVs

and indels (b) and CNAs (c).

Figure 5. Confirmation of DS variants detected in clinical samples by tissue genotyping. (a)

Positive predictive value of DS calls relative to tissue genotyping results derived from patient

medical records across 543 clinical samples.




21

Figure 6. Clinical Digital Sequencing of 10,585 advanced cancer patients. (a) Cohort descriptive

statistics. (b) Proportion of clinical samples by tumor type. Diamonds indicate ctDNA detection

rate for each tumor type. (c) Distribution of tumor ctDNA burden as represented by the

maximum VAF or copy number of a somatic variant in a given sample. (d) Somatic alteration

prevalence by variant type and gene. (e) Prevalence of alterations associated resistance to anti-

EGFR monoclonal antibody therapy. (f)Targetable alteration prevalence in 4,521 consecutive

non-squamous NSCLC patients. CUP, carcinoma of unknown primary; SCLC, small cell lung

cancer; CNS, central nervous system.




Published OnlineFirst April 24, 2018.Clin Cancer Res Justin I Odegaard, John J Vincent, Stefanie Mortimer, et al. methodologiesassay utilizing orthogonal tissue- and plasma-based Validation of a plasma-based comprehensive cancer genotyping

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